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ProcessGAN : Supporting the creation of business process improvement ideas through generative machine learning

Title data

van Dun, Christopher ; Moder, Linda ; Kratsch, Wolfgang ; Röglinger, Maximilian:
ProcessGAN : Supporting the creation of business process improvement ideas through generative machine learning.
In: Decision Support Systems. (2022) . - No. 113880.
ISSN 1873-5797
DOI: https://doi.org/10.1016/j.dss.2022.113880

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information
Projektgruppe WI Wertorientiertes ProzessmanagementNo information

Abstract in another language

Business processes are a key driver of organizational success, which is why business process improvement (BPI) is a central activity of business process management. Despite an abundance of approaches, BPI as a creative task is time-consuming and labour-intensive. Most importantly, its level of computational support is low. The few computational BPI approaches hardly leverage the opportunities brought about by computational creativity, neglect process data, and rely on rather rigid improvement patterns. Given the increasing amount of process data in the form of event logs and the uptake of generative machine learning for automating creative tasks in various domains, there is huge potential for BPI. Hence, following the design science research paradigm, we specified, implemented, and evaluated ProcessGAN, a novel computational BPI approach based on generative adversarial networks that supports the creation of BPI ideas. Our evaluation shows that ProcessGAN improves the creativity of process designers, particularly the originality of BPI ideas, and shapes up useful in real-world settings. Moreover, ProcessGAN is the first approach to combine BPI and computational creativity.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Business process improvement; Business process redesign; Generative adversarial networks; Generative machine learning; Process mining
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management > Chair Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 22 Nov 2022 06:53
Last Modified: 22 Nov 2022 07:34
URI: https://eref.uni-bayreuth.de/id/eprint/72872